Association of Hospital Adoption of Probiotics With Outcomes Among Neonates With Very Low Birth Weight

This cohort study examines changes in probiotic use among neonates with very low birth weight in US neonatal intensive care units (NICUs) between 2012 and 2019 and the association of routine probiotic use in this population with health outcomes.

The first set of estimates uses an event-study framework to compare trends in infant health outcomes across adopting and non-adopting hospitals. We denote health outcomes ℎ (including mortality, necrotizing enterocolitis, and sepsis) for infant admitted to hospital NICU ℎ at time . Further, define (ℎ, ) as the event year for an adopting hospital, with (ℎ, ) = −1 in the year before adoption, (ℎ, ) = 0 during the first year of adoption, and so on. We estimate fixed effect logit regressions of the following form: Pr( ℎ = 1) = ( (ℎ, ) ℎ + ℎ + + ) The variable ℎ equals 1 for hospitals that adopt probiotics between 2013-2019, and equals 0 for all other hospitals. The vector (ℎ, ) identifies the comparison of adopting to non-adopting hospitals in relative event year (ℎ, ). We exclude the (ℎ, ) = −1 category from the regression estimation, so that the comparison −1 is normalized to zero in the year prior to adoption. Non-adopting hospitals and hospitals that adopted in 2012 or earlier have (ℎ, ) = 0. The regression includes hospital fixed effects ℎ to allow for fixed differences across hospitals in patient outcomes. The regression also includes year fixed effects . The inclusion of hospital and year fixed effects ensures that variation identifying adoption effects comes from within-hospital changes in infant outcomes, comparing trends in adopting vs. non-adopting hospitals. Finally the regression includes infant characteristics including birth weight, gestational age, SGA, race, sex, multiple, location of birth, 1-minute APGAR score, major birth defect. These controls help account for potential time-varying differences in patterns of patient sorting across NICUs.
We plot the coefficients (ℎ, ) in Figure 3. These plots allow us to assess the parallel trends assumption visually. If health outcomes at adopting and non-adopting hospitals have parallel trends, then we would expect no differential changes in infant outcomes over the pre-period at adopting hospitals. We formalize this with an F-test for whether the values (ℎ, ) are jointly equal to 0 over the pre-period, i.e. for all < −1. We also use the specification in equation (1) to calculate a single estimate of association between NICU-level probiotic use and infant outcomes: we difference the average value of (ℎ, ) for 1 ≤ ≤ 4 with the average value of (ℎ, ) for −4 ≤ ≤ −1. This estimates the average effect of probiotic adoption, comparing the four years after adoption to the four years before.
Our second specification makes use of the continuous variation in probiotic use across hospitals and over time, thus accounting for the fact that we would expect a smaller decline in NEC at a hospital where only 20% of VLBW infants receive probiotics relative and a larger decline at a hospital where 80% of VLBW infants receive probiotics. We estimate a fixed effect logit regression of the following form: The treatment variable − ,ℎ calculates the proportion of infants treated in that hospital-year who receive probiotics, excluding the index infant; this proportion ranges can range from 0 (no infants receive probiotics) to 1 (all infants receive probiotics). We exclude the index infant from this calculation to avoid bias that can arise from correlation between the infant's own health status and the decision to treat that infant with probiotics. 1 As before, the regression includes hospital fixed effects ℎ , year fixed effects , and a vector of infant characteristics . estimates the effect of probiotics on treated infants, and is identified from within-hospital over-time variation in the hospital-level probiotic utilization rate, relative to over-time changes at non-adopting hospitals. Results from this specification are plotted in Figure 4, in the top row "Overall" results.
To investigate potential heterogeneity in treatment effects, we consider three further modifications to this specification that interact the − ,ℎ variable with an infant level characteristic. For example, we estimate: The variable equals 1 if the infant is extremely low birth weight and equals 0 otherwise. This specification allows us to test whether the benefits of being treated at a hospital with high probiotic use are different for ELBW infants. Note that is included among the infant characteristics , in all specificaitons. In two additional regressions, we similarly test whether infants born by Cesarean section vs. vaginal deliveries have different benefits of being treated at a hospital with high probiotic use, and whether infants receiving any breast milk vs. formula alone have different benefits of being treated at a hospital with high probiotic use. (Recall that mode of delivery and breastmilk exposure are also among the control variables included in .) These results are reported in the bottom three panels of Figure 4 and in eTable2.
To investigate whether the composition of infants treated at adopting NICUs is changing around the time of probiotic adoption, we estimate linear regressions of the following form: = − ,ℎ + ℎ + + ℎ This equation tests whether infant characteristics are changing differentially at adopting NICUs relative to non-adopting NICUs, in a way that is correlated with the timing of probiotic adoption. Although we can directly control for any such changes in observable infant characteristics, we use this test as a proxy for investigating potential selection on unobservable characteristics. Results are reported in eTable 3 column 1.
We contrast our approach focusing on hospital-level probiotic adoption with an alternative approach that investigates patterns of probiotic use within adopting NICUs, using the following equation: = + ℎ + + ℎ This equation tests whether infants receiving probiotics at adopting NICUs are similar in their observable characteristics to infants who do not receive probiotics. This specification highlights the potential for confounding factors to bias any direct comparisons of treated and untreated infants. These results are reported in eTable 3 column 2.
We test alternative specifications in eTable 4. In Panel A we report our original estimates of equation 2 for comparison. In Panel B, we re-estimate equation 2, with a new sample restriction to include only hospitals that provide registry data to the VON in all eight years. In Panel C, we re-estimate equation 2, now eliminating early adopting hospitals that already treat at least 20% of their infants with probiotics as of 2012, the earliest year in our data. In Panel D, we estimate a simple difference-in-differences regression with two-way fixed effects. This is a variation on estimating equation (2) above, but replacing the continuously varying − ,ℎ variable with a binary variable 20 ℎ which equals 1 if the hospital has crossed the 20% probiotic adoption threshold in the current year or any prior year. 20 ℎ equals 0 prior to probiotic adoption, and is always 0 for non-adopting hospitals. Results with the binary adoption variable are similar to the specifications using continuous variation in adoption status, which is consistent with the high rates of probiotic use among adopting hospitals. We also investigate the robustness of these results to using a different threshold of probiotic use to define adoption. In Panel E, we repeat the analysis but define a hospital as post-adoption if it has provided probiotics to at least 10% of VLBW infants in the current year or any prior year. eFigures and eTables eFigure. Trends over time in probiotics use, NEC, sepsis and mortality by NICU adoption status eTable 1. Descriptive data on characteristics of non-adopting, newly-adopting, and early adopting hospitals Notes: This table reports results (expressed as odds ratios) and standard errors from 12 separate logit regressions; these are the same results displayed graphically in Figure 4. The outcome variable of the logit regression listed in the column headers (NEC, sepsis, or mortality). The independent variable of interest in Panel A is the rate of probiotic use among other VLBW infants in the same NICU-year. For specifications reported in Panels B,C, and D, the probiotic use rate is interacted with infant characteristics. All regressions control for calendar year fixed effects, hospital fixed effects, and neonate characteristics (birth weight, gestational age, SGA, race, sex, multiple, location of birth, 1-minute APGAR score, and major birth defect). Sample size: 307,905 infants. ***indicates statistical significance at the 1% level. *indicates statistical significance at the 10% level.